A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation.

ABSTRACT: Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical downscaling methods infer these phenomena as temporally decoupled spatial patches. However, to preserve physical properties, estimating spatio-temporally coherent high-resolution weather dynamics for multiple variables across long time horizons is crucial. We present a novel generative framework that uses a score-based diffusion model trained on high-resolution reanalysis data to capture the statistical properties of local weather dynamics. After training, we condition on coarse climate model data to generate weather patterns consistent with the aggregate information. As this predictive task is inherently uncertain,

SUBMITTER: Schmidt J 

PROVIDER: S-EPMC12270901 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature